ArticleCardiovascular engineering and technology2018
Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis.
Article in Cardiovascular engineering and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Agent-based modeling of cellular dynamics in adoptive cell therapy.Communications biology · 2026Article
- A predictive multiscale model of in-stent restenosis in femoral arteries: linking haemodynamics and gene expression with an agent-based model of cellular dynamics.Journal of the Royal Society, Interface · 2022Article
- Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling.Journal of the Royal Society, Interface · 2022Article
- Uncertainty quantification patterns for multiscale models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models.Frontiers in bioengineering and biotechnology · 2021Review
- [Review of studies on the biomechanical modelling of the coupling effect between stent degradation and blood vessel remodeling].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- Location-Specific Comparison Between a 3D In-Stent Restenosis Model and Micro-CT and Histology Data from Porcine In Vivo Experiments.Cardiovascular engineering and technology · 2019Article
- Multiscale modelling, simulation and computing: from the desktop to the exascale.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
- Semi-intrusive multiscale metamodelling uncertainty quantification with application to a model of in-stent restenosis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
- Multiscale computing for science and engineering in the era of exascale performance.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
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Authors and funding
4 authors.
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Abstract
purposeCoronary artery stenosis, or abnormal narrowing, is a widespread and potentially fatal cardiac disease. After treatment by balloon angioplasty and stenting, restenosis may occur inside the stent due to excessive neointima formation. Simulations of in-stent restenosis can provide new insight into this process. However, uncertainties due to variability in patient-specific parameters must be taken into account.
methodsWe performed an uncertainty quantification (UQ) study on a complex two-dimensional in-stent restenosis model. We used a quasi-Monte Carlo method for UQ of the neointimal area, and the Sobol sensitivity analysis (SA) to estimate the proportions of aleatory and epistemic uncertainties and to determine the most important input parameters.
resultsWe observe approximately 30% uncertainty in the mean neointimal area as simulated by the model. Depending on whether a fast initial endothelium recovery occurs, the proportion of the model variance due to natural variability ranges from 15 to 35%. The endothelium regeneration time is identified as the most influential model parameter.
conclusionThe model output contains a moderate quantity of uncertainty, and the model precision can be increased by obtaining a more certain value on the endothelium regeneration time. We conclude that the quasi-Monte Carlo UQ and the Sobol SA are reliable methods for estimating uncertainties in the response of complicated multiscale cardiovascular models.
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